Most face images in real life are occluded, which often leads to the loss of key information of the face to be detected. Aiming at the problem of difficulty in extracting facial features due to occlusion in the process of face recognition, this paper designs a occluded face detection algorithm based on VGGNet and multi-feature point fusion. This method uses the VGG-16 framework as the backbone network for feature extraction, and adds the occlusion processing unit OCC-Net (Occlusion-Net) before the input of the fully connected layer of the traditional VGG network. In this layer, the method of multi-feature fusion is first adopted to enhance the network's extraction of facial features; then the scale-invariant feature transformation (SIFT) algorithm is used to expand the small-scale feature maps in the network to obtain richer complementary information and improve The traditional VGG network has a serious problem of small-scale feature loss caused by multiple convolution and pooling operations; finally, the regression box parameters are improved to reduce the sensitivity of the loss function to the occluded area, and the position information of the occluded area is obtained through border regression. Improved the accuracy of face detection in the presence of occlusion. The experimental results show that compared with commonly used algorithms such as PCANet, Faster RCNN, and traditional VGGNet without OCCNet, the algorithm in this paper can more accurately locate the occluded face on the commonly used occlusion data sets such as FDDB and RMFD, which confirms The effectiveness and robustness of the algorithm.